ConlangCrafter: AI That Builds Brand-New Languages with Modular LLM Problem-Solving

ConlangCrafter, a new tool from researchers at the University of Miami and collaborators, uses large language models to construct brand-new languages with their own sounds, grammar, and vocabulary. Instead of generating a language in one shot, the system decomposes the task into smaller sub-problems, solves each with an LLM, and combines the results into a coherent language with a running grammar rulebook. For AI builders, this modular approach to complex generation tasks is a pattern worth studying.

What happened

The tool, presented at the Proceedings of the Association for Computational Linguistics, was developed by Morris Alper, Moran Yanuka, Raja Giryes, and Gašper Beguš. Alper reports that the team has already created over 60 languages using ConlangCrafter, and the code is publicly available. Users can hand ConlangCrafter specific instructions, such as a language with zero consonant sounds or one designed for an alien species communicating through color and gesture. Once a language takes shape, ConlangCrafter translates sentences into it, checks its own work for inconsistencies, and updates a running grammar rulebook. The key insight, according to Alper, is to "split the problem apart and have the LLMs solve each sub-problem and combine them together."

Separately, South Korea announced the AI for Everyone project on July 13, aiming to provide free AI chatbots and a government-navigation platform built around locally developed models. Policymakers continue to worry about emotional bonds with AI companions and potential misuse. In a joint cybersecurity advisory, the NSA, CISA, FBI, and international partners warn that hackers linked to Center 16 of Russia's Federal Security Service are continuing to target vulnerable networking equipment in critical infrastructure sectors like energy, healthcare, and government.

Why AI builders should care

The ConlangCrafter approach demonstrates a practical pattern for tackling large creative tasks with LLMs: decompose the problem, solve each sub-problem independently, and integrate the solutions. This is directly applicable to any AI product that needs to generate complex, structured outputs, such as game content, procedural narratives, or synthetic data for training. The public code lowers the barrier for experimenting with language generation and could inspire similar modular workflows in other domains.

Practical implications

For writers and filmmakers, ConlangCrafter offers a way to generate convincing fictional languages for fantasy or sci-fi worlds, similar to the constructed languages in Game of Thrones or Lord of the Rings. Researchers studying under-documented languages or language evolution could use AI-assisted language construction to test hypotheses or generate controlled datasets. The tool's ability to enforce consistency through a grammar rulebook makes it more reliable than a single-pass generation.

Caveats

The capabilities demonstrated are based on the authors' paper and demonstrations; the long-term linguistic validity and stability of the generated languages are not yet established. The broader policy and ethical considerations around AI companions and misuse, as highlighted in related coverage, apply to any AI system that generates human-like content. Additionally, the cybersecurity advisory underscores the importance of securing infrastructure when deploying AI tools in production environments.